000			nam a22 4500
999			_c32499 _d32499
008			230901b xxu||||| |||| 00| 0 eng d
020			_a9783031046476
082			_a006.31 _bUNP
100			_aUnpingco, Jose
245			_aPython for probability, statistics, and machine learning
250			_a3rd ed.
260			_bSpringer, _c2022 _aCham :
300			_axii, 509 p. ; _bill., (some col.), _c25 cm.
365			_b84.99 _cEUR _d94.90
504			_aIncludes bibliographical references and index.
520			_aUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.
650			_aStatistics Data processing
650			_aData mining
650			_aDiscrete mathematics
650			_aTelecommunications
650			_aBernstein von-Mises theorem
650			_aCentral limit theorem
650			_a Delta Method
650			_aFisher Ecact Test
650			_aGeneralized Linear Models
650			_a Hazard functions
650			_aInverse CDF Method
650			_aJupyter notebook
650			_aKernel trick
650			_aLogilinear models
650			_aMann-Whitney -Wilcoxen Test
650			_aNeyman-Pearson test
650			_aPlug-in principle
650			_aRejection Method
650			_a Uniqueness theorem
650			_a Wald Test
942			_2ddc _cBK